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Hugging Face

 Public datasets available on Hugging Face cover a wide range of applications, from natural language processing to computer vision. This guide will help you explore and use these datasets effectively for your machine learning projects.

Loading a Dataset  
Install the Datasets Library: 

from datasets import load_dataset

dataset = load_dataset('dataset_name')


View Dataset Structure: 
print(dataset)

Accessing Specific Splits:
train_dataset = dataset['train'] print(train_dataset[0])  



            from transformers import AutoTokenizer

           tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') tokenized_dataset =                   dataset.map(lambda x: tokenizer(x['text'], padding="max_length", truncation=True))





import matplotlib.pyplot as plt

plt.hist([len(x['text']) for x in dataset['train']], bins=20) plt.show()

An overview of the datasets available on Hugging Face, including how to search for and load datasets into your projects. The article will feature examples from popular datasets used in NLP, computer vision, and audio tasks.

Exploring and Using Public Datasets

                pip install datasets

             

from datasets import load_dataset

           dataset = load_dataset('dataset_name')



       View Dataset Structure
         print(dataset) 

Accessing Specific Splits
train_dataset = dataset['train'] 
print(train_dataset[0])




A guide on creating custom datasets, including data collection, formatting, and uploading to the Hugging Face Hub. The article will also cover dataset documentation and sharing best practices.

Creating and Sharing Your Own Datasets

 Creating and sharing your own datasets on Hugging Face allows you to contribute to the community and make your data available for collaborative use. This guide outlines the steps to create, upload, and share your datasets.

import pandas as pd

data = pd.read_csv('your_dataset.csv')

from datasets import Dataset

dataset = Dataset.from_pandas(data)



Login to Hugging Face

huggingface-cli login

Create a New Repository:
from huggingface_hub import HfApi

api = HfApi()
api.create_repo(name="your_dataset_name")

Techniques for preprocessing datasets to prepare them for machine learning models. This article will include methods for handling missing data, normalization, tokenization, and augmentation for various data types.